Remarkably, despite the surge in research on the use of FL, only a handful of works in [1]}, [2]}, [3]}, [4]}, [5]}, [6]} have studied the energy efficiency of FL from a system-level perspective. A novel analytical framework that derived energy efficiency of FL algorithms in terms of the carbon footprint was proposed in [1]}. Meanwhile, in [2]}, the authors formulated an energy minimization problem under heterogeneous power constraints of mobile devices. The work in [3]} investigated a resource allocation problem to minimize the total energy consumption considering the convergence rate. In [4]}, the energy consumption of FL was minimized by controlling workloads of each device, which has heterogeneous computing resources. The work in [5]} proposed a quantization scheme for both uplink and downlink transmission in FL and analyzed the impact of the quantization on the convergence rate. The authors in [6]} considered a novel FL setting, in which each device trains a binary neural network so as to improve the energy efficiency of transmission by uploading the binary parameters to the server.
